SOTAVerified

Domain Generalization

The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain

Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning

Papers

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Showing 1–50 of 1751 papers

TitleStatusHype
DINOv2: Learning Robust Visual Features without SupervisionCode6
Matching Anything by Segmenting AnythingCode5
Sequencer: Deep LSTM for Image ClassificationCode5
A ConvNet for the 2020sCode5
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPOCode4
Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and BeyondCode4
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive ReinforcementCode4
Conditional Prompt Learning for Vision-Language ModelsCode4
Deep Residual Learning for Image RecognitionCode4
Generalized Trajectory Scoring for End-to-end Multimodal PlanningCode3
Distilling LLM Agent into Small Models with Retrieval and Code ToolsCode3
Reinforcement Learning for Reasoning in Large Language Models with One Training ExampleCode3
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation ModelsCode3
ControlMLLM: Training-Free Visual Prompt Learning for Multimodal Large Language ModelsCode3
Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic SegmentationCode3
Generative Data Augmentation using LLMs improves Distributional Robustness in Question AnsweringCode3
MetaFormer Baselines for VisionCode3
EfficientNet: Rethinking Model Scaling for Convolutional Neural NetworksCode3
AutoAugment: Learning Augmentation Policies from DataCode3
Feed-Forward SceneDINO for Unsupervised Semantic Scene CompletionCode2
Play to Generalize: Learning to Reason Through Game PlayCode2
Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System CollaborationCode2
Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General ReasoningCode2
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency AdaptationCode2
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object DetectionCode2
Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic SegmentationCode2
DeepPerception: Advancing R1-like Cognitive Visual Perception in MLLMs for Knowledge-Intensive Visual GroundingCode2
Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image SegmentationCode2
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain GeneralizationCode2
FAMNet: Frequency-aware Matching Network for Cross-domain Few-shot Medical Image SegmentationCode2
SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation LearningCode2
OpenAD: Open-World Autonomous Driving Benchmark for 3D Object DetectionCode2
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic SegmentationCode2
PaPaGei: Open Foundation Models for Optical Physiological SignalsCode2
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable DiffusionCode2
HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure ModelingCode2
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question AnsweringCode2
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge DistillationCode2
GalLoP: Learning Global and Local Prompts for Vision-Language ModelsCode2
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition MonitoringCode2
Continuous Temporal Domain GeneralizationCode2
VimTS: A Unified Video and Image Text Spotter for Enhancing the Cross-domain GeneralizationCode2
Generative Medical SegmentationCode2
Neural Markov Random Field for Stereo MatchingCode2
Single Domain Generalization for Crowd CountingCode2
Robust Synthetic-to-Real Transfer for Stereo MatchingCode2
Gradient Alignment for Cross-Domain Face Anti-SpoofingCode2
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction DataCode2
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All datasetsPACSVizWiz-ClassificationImageNet-COffice-HomeImageNet-AImageNet-RDomainNetVLCSTerraIncognitaGTA-to-Avg(Cityscapes,BDD,Mapillary)ImageNet-SketchGTA5 to Cityscapes

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SIMPLE+Average Accuracy99—Unverified
2PromptStyler (CLIP, ViT-L/14)Average Accuracy98.6—Unverified
3GMDG (RegNetY-16GF, SWAD)Average Accuracy97.9—Unverified
4D-Triplet(RegNetY-16GF)Average Accuracy97.6—Unverified
5MoA (OpenCLIP, ViT-B/16)Average Accuracy97.4—Unverified
6GMDG (e RegNetY-16GF)Average Accuracy97.3—Unverified
7PromptStyler (CLIP, ViT-B/16)Average Accuracy97.2—Unverified
8SPG (CLIP, ViT-B/16)Average Accuracy97—Unverified
9CAR-FT (CLIP, ViT-B/16)Average Accuracy96.8—Unverified
10MIRO (RegNetY-16GF, SWAD)Average Accuracy96.8—Unverified
#ModelMetricClaimedVerifiedStatus
1ViT-8/B-224Accuracy - Clean Images450—Unverified
2VOLO-D5Accuracy - All Images57.2—Unverified
3ConvNeXt-BAccuracy - All Images53.5—Unverified
4ResNeXt-101 32x16dAccuracy - All Images51.7—Unverified
5EfficientNet-B8 (advprop+autoaug)Accuracy - All Images50.5—Unverified
6EfficientNet-B7 (advprop+autoaug)Accuracy - All Images49.7—Unverified
7EfficientNet-B6 (advprop+autoaug)Accuracy - All Images49.6—Unverified
8EfficientNet-B5 (advprop+autoaug)Accuracy - All Images49.1—Unverified
9ViT-16/L-224Accuracy - All Images49—Unverified
10ResNet-50 (gn)Accuracy - All Images48.9—Unverified